IJE TRANSACTIONS B: Applications Vol. 28, No. 11, (November 2015) 1552-1563

Please cite this article as: H. Esfandian, M. Parvini, B. Khoshandam, A. Samadi-Maybodi, Removal of Diazinon from Aqueous Solutions in Batch Systems Using Cu-modified sodalite zeolite: An Application of Response Surface Methodology, International Journal of Engineering (IJE), TRANSACTIONS B: Applications Vol. 28, No. 11, (November 2015) 1552-1563

International Journal of Engineering

J o u r n a l H o m e p a g e : w w w . i j e . i r

Removal of Diazinon from Aqueous Solutions in Batch Systems Using Cu-modified

Sodalite Zeolite: an Application of Response Surface Methodology H. Esfandiana, M. Parvinia*, B. Khoshandama, A. Samadi-Maybodib a Faculty of Chemical, Oil and Gas Engineering, Semnan University, Semnan, Iran.

b Analytical Division, Faculty of Chemistry, University of Mazandaran. Babolsar, Iran

P A P E R I N F O

Paper history: Received 03 October 2015 Received in revised form 05 November 2015 Accepted 19 November 2015

Keywords: Diazinon Adsorption Sodalite Zeolite Batch Systems RSM

A B S T R A C T

In this work, perlite was used as a low-cost source of Si and Al in the synthesis of sodalite zeolite using hydrothermal synthesis method. Cu2O nanoparticles were coated on a bed of sodalite zeolite and used

as an adsorbent for removal of diazinon from aqueous solutions. To analyze the process, a significant

variable .i.e. removal efficiency (%) of diazinon and three dependent parameters as the process responses were examined through a central composite design (CCD) under the response surface

methodology (RSM). The optimum conditions included: 0.22 g adsorbent, 23.62 min contact time, at

29.28 oC. The percentage removal of diazinon in batch administrations was 97.24.

doi: 10.5829/idosi.ije.2015.28.11b.02

1. INTRODUCTION1

The presence of pesticides in soils, ground water and

surface water is currently an important concern

throughout the world because many of these materials

are harmful to both human health and the environment.

Increasing application of pesticides in agriculture fields

for controlling pests is polluting water resources every

day [1, 2]. Organophosphate pesticides are among

insecticides with widespread application in pest control

in the many agricultural areas. These pesticides

originate from different sources especially from

agricultural drainage [2]; wastewater treatment plants

[3] and other water resources. These pesticides are

mainly nonbiodegradable environmental pollution and

are carcinogenic in nature. Therefore, pesticides toxicity

and their post-degradation products that make serious

contribution to increased levels of environmental and

water contamination have become predominant all

around the world [4]. Diazinon is considered as a kind

of organophosphate pesticide. Diazinon is utilized as a

1*Corresponding Author’s Email: m.parvini@ semnan.ac.ir. (M.

Parvini)

control measure for pests in fruits, vegetables, and field

crops, but excessive concentration of this insecticide has

detrimental effect on organisms and blood. Hence, the

amount of usage should be carefully determined in case

the toxicant contaminate ground or sea water [5, 6]. At

the same time, diazinon is easily absorbed into the skin

and holds a synergic characteristic with other toxins

such as pyrethrines [7]. A maximum allowed

concentration of 0.5 μg/L was determined by the

European Union for the combination of all pesticides

and 0.1 μg/L for individual compounds in drinking

water [8]. Several techniques were employed to remove

the toxicant from water, such as treatment by adsorption

processes [9, 10], advanced oxidation processes [11, 12]

and electrocoagulation process [6]. Adsorption reveals

one of the most effective methods for the removal of

pollutants from the environment. This technique uses an

equipment that is easy to use and readily available, yet

not energy intensive. The treatment is also cost effective

[13-15]. Various adsorbents were utilized for the

elimination of diazinon from the water and wastewater

such as agricultural soil [16], surfactant modified

agricultural soil [17], Organo-Zeolites [18] and

modified Bentonite [19]. Zeolites are one of the capable

1553 H. Esfandian et al./ IJE TRANSACTIONS A: Applications Vol. 28, No. 11, (November2015) 1552-1563

materials for the adsorption of organic compounds. In

the case that the volume with a high concentration of

organic compounds, the zeolite is used as a sorbent that

can be then reused or recycled. For the low

concentrations of organic compounds, the sorption can

be combined with an oxidative catalyze process.

Advantage of zeolite application rather than carbon

bases such as activated carbon is its capability to be

used for the flow which contains fewer pollutant

concentration and moisture. In these cases, application

of the zeolites with a high amount of silica which are

hydrophobic, are so effective [20]. The nanoparticles

are widely applied as catalyst in various fields and

environmental issues. For an example of air pollution

controlling case, Catalytic incineration of benzene on

metal oxide catalysts has been studied and the results

showed a complete destruction of benzene at 300°C and

5.5% (weight percent) of copper nanoparticles on TiO2.

The results of a research to complete oxidation of

naphthalene on metal oxide catalysts, illustrated the

catalytic properties of metal oxides such as CuO in

naphthalene removal [21]. Some other researches have

demonstrated destruction of the volatile organic

compounds by iron nanoparticles [22, 23]. Perlite is a

rhyolitic glass the structure of which is made up of more

than 70% silica and 13% alumina. The amounts of silica

and alumina are different in various types and forms

[24]. After heating, perlite changes to a light weight

material and is typically light grey to white in color and

is named ‘‘expanded’’ perlite. Perlite has various

properties due to the differences in composition and

structure. Among the countries that produce perlite, Iran

is considered the largest producer [24]. Perlite is

commercially low in price as a result of its abundant

utilization as in ceiling tile, pipe insulation, gypsum

wallboard, cryogenic insulation and filter media, etc.

[25]. Zeolites were successfully utilized in the chemical

industry and environmental protection over the last 35

years in accordance with their significant physical and

chemical properties which include molecular sieving,

adsorbing and cation exchange capability [25].

Preparation of zeolites from low-cost sources has been

the main objective in many researches [26]. Many

research groups were formed to prepare zeolites from

perlite in laboratories that led to the formation of

different types of zeolites, such as zeolite-X,

gismondine, heulandite, ZSM-5, etc [27]. A natural

zeolite (bentonite) was used in Bensaadi Ouznadji et al.

[19] for the sorption of diazinon from aqueous

solutions. The latter objective was to explain the effects

of different parameters such as pH, temperature, contact

time, adsorbent dosage, and initial pesticide

concentration on the diazinon removal. In another study,

the possibility of using MCM-41 and MCM-48

mesoporous silicas was examined for the diazinon and

fenitothion removal from non-polar solvent via the

batch sorption method [28]. The organ-zeolite was also

employed as a low cost sorbent for the removal of

atrazine, lindane and diazinon from water. In addition,

the effect of different operating variables was studied on

the sorption of atrazine, lindane and diazinon onto

organo-zeolite and equilibrium isotherm of this sorption

process [18]. The main objective in this research was to

examine the synthesized sodalite zeolite obtained from

perlite. Sodalite zeolite was modified by Copper oxide

(Cu2O) nanoparticles and utilized as an attractive

adsorbent for the diazinon pesticide treatment from

aqueous solution. Moreover, many statistical and

experimental designs were recognized as useful tools to

optimize the variables of diazinon sorption onto

modified zeolite.

2. MATHEMATICAL MODEL 2. 1. Reagents and Chemicals In this

experiment, the diazinon (95–96% active substance)

was provided by Merck. Diazinon solution of 50 mg/L

was prepared in deionized water and kept in a

refrigerator at 3 oC until use. Table 1 shows the

chemical and physical properties of diazinon. Using

H2SO4 (1 M) and NaOH (1M), the solution pH was

adjusted. Diazinon concentration of the aqueous

solution was determined using the Uv-vis

spectrophotometry technique. Spectra were recorded at

maximum wavelength (λmax) 247 nm [6, 28]. Uv-vis of

diazinon are presented in Figure 1. The adsorbent,

namely perlite, was provided by Afrazand Company,

Iran. The source of this perlite was Meyaneh, Iran.

Copper oxide (Cu2O) with a 30-60 nm particle size was

purchased from Merck. 2. 2. Synthesis of Sodalite Zeolite A starting

material such as raw perlite was employed with a grain

size of <75 μm (72.68 wt.% SiO2; 11.74 wt.% Al2O3).

TABLE 1. Chemical and physical properties of diazinon.

Property Information

Chemical class Organophosphate

Chemical fomula C12H21N2O3PS

Molecular weight 304.345502 g/mol

Color Clear colorless liquid

Physical state Liquid

Boiling point 83–84 °C at 2x10-3

mm Hg;

decomposes at > 120 oC

Specific Denisty 1.11 (20 °C)

Melting Point Decomposes at >120°C

H. Esfandian et al. / IJE TRANSACTIONS A: Applications Vol. 28, No. 11, (November 2015) 1552-1563 1554

Figure 1. UV–vis spectrum of a diazinon solution.

First, the aluminate solution was prepared after

0.873 g Al-foil added to 15 mL of 2.7 N NaOH solution

was dissolved. Then, the silicate solution was prepared

by mixing 3.651 g of perlite which was later transferred

to a polypropylene bottle containing 31 mL of 2.7N

NaOH solution. Through magnetic stirring, the mixture

was heated for 2h at a constant temperature of 70 °C.

Then, the suspension was filtered. Next, the silica

source was added slowly to the alumina source. The

resulting mixture was stirred for 60 min to obtain a

homogenous and clear solution. Afterwards, the mixture

was transferred into a stainless steel autoclave with

teflon tube and heated in an oven at 110 °C for 25 h.

After the finalization of the hydrothermal treatment, the

products were collected by means of filtration, washed

with deionized water and dried at 80 °C overnight.

2. 3. Synthesis of Cu Modified Sodalite Through the grains draining in a dispersed suspension of

nanoparticles, coating of nanoparticles on zeolite grains

was carried out. Typically, 0.1gr Copper oxide (Cu2O)

with a particle size of 30-60 nm was spilled into an

Erlenmeyer flask containing 10 mL distilled water and

later sonicated a few minutes to make a uniform

suspension. Then, 2 gr sodalite zeolite was added to the

flasks and shook moderately for 2h. Ultimately, the

contents of the flasks were dried slowly at 80°C for 10h.

The added value of nanoparticles was 4.5 wt% of zeolite

[29, 30].

2. 4. Instrumentation In the present study, X-

ray diffraction (XRD) patterns were examined by a

GBC MMA diffractometer (MMA, GBC Scientific

Equipment LLC, USA) through CuKα radiation. Scans

were recorded in an angular range from 5° to 70°. To

study the surface of the sodalite zeolite, Scanning

Electron Microscope (SEM) model S3400, Hitachi,

Japan was employed. Gold and palladium were used to

coat the sample using a sputter coater with conductive

materials in order to improve the image quality. The

thickness and density of coating were 30.00 nm and

19.32 g/cm3, respectively. Energy dispersive X-ray

(EDX) spectra were recorded on an EDX Genesis XM2

attached to SEM. The sodalite infrared spectra (IR)

were obtained by FTIR Spectrometer (Shimadzu 4100)

to determine the sodalite functional groups. Using KBr

pellets, spectra were collected with a spectrometer. In

each case, the homogenization of 1.0 mg of dried

sodalite and 100 mg of KBr was done using mortar and

pestle. Subsequently, they were pressed onto a

transparent tablet at 200 kgf/cm2 for 5 min. The pellets

are characterized by a FTIR spectrometer in the

transmittance (%) mode with a scan resolution of 4 cm−

1 in the range 4200–500 cm

− 1.

2. 5. Experimental Design The percent

removal of diazinon from aqueous solution in batch

system was designed by RSM (response surface

methodology) package using the Design-Expert

software 7.01 (Stat-Ease Inc., Minneapolis, MN, USA)

[31]. The statistical method of factorial design of

experiments (DOE) removes systematic errors together

with an estimation of the experimental error and

minimization of the number of experiments [32, 33].

The central composite design (CCD) is mostly used

under the RSM design. RSM was employed to evaluate

the relationship between responses and independent

variables and to optimize the relevant conditions of

variables with the aim of predicting the best value of

responses. By solving the regression equation at the

desired values of the process responses as the

optimization criteria, the optimum values of the selected

variables were obtained [34]. Each independent factor

was coded at five levels: −α, −1, 0, +1 and +α. The

variable range and level are given in Table 2 in coded

units resulting from RSM studies [35]. The design was

composed of 2k factorial points augmented by 2k axial

points and a center point where k is the number of

variables. Thus, 20 experiments (2k +2k+6=20) were

performed with 15 experiments. Three dependent parameters of the experiments

conducted in batch conditions were measured directly or

calculated as response. After analyzing the variances

(ANOVA), the regression equation yielded the level of

diazinon removal efficiency (%) as a function of contact

time (A), amount of adsorbent (B) and temperature (°C).

This table presents the result of the CCD experiments

that are obtained through studying the effect of three

independent variables together with the predicted mean

and observed responses. The results are calculated using

the Design Expert software (Table 3).

TABLE 2. Experimental ranges and levels of the independent

variables.

Range and Level Name of variables Type of variable

+1 0 -1

30 16 2 Contact time(min)

Numerica 0.3 0.17 0.05 Amount of adsorbent(g)

50 35 20 Temperature (oC)

1555 H. Esfandian et al./ IJE TRANSACTIONS A: Applications Vol. 28, No. 11, (November2015) 1552-1563

TABLE 3. Full factorial central composite design matrix of

orthogonal and real values along with observed .

Run

Variable with coded levels Response diazinon

removal (%)

Contact

time

(min)

Amount

of

adsorbent

(g)

Temperature

(oC) Experimental Predicted

1 0 0 0 83.24 85.58

2 -1 1 -1 38.91 41.09

3 -1 -1 -1 29.35 29.19

4 -1 0 0 53.21 49.42

5 0 0 1 71.68 72.51

6 1 1 -1 96.71 97.86

7 0 -1 0 53.91 58.24

8 1 -1 -1 60.07 59

9 1 1 1 73.47 74.15

10 0 0 0 83.52 85.58

11 0 0 0 84.35 85.58

12 0 0 0 84.25 85.58

13 0 1 0 90.12 83.7

14 0 0 0 89.21 85.58

15 1 -1 1 38.41 35.94

16 1 0 0 84.21 85.91

17 0 0 0 84.71 85.58

18 -1 -1 1 19.54 18.91

19 0 0 -1 92.43 89.51

20 -1 1 1 29.39 30.98

2. 6. Mathematical Modeling The experimental

data were subjected to multiple regression analyses and

the CCD design experimental results matched a full

second-order polynomial equation [36]. The

experiments revealed that each parameter could only

come in three levels and the appropriate model was the

quadratic equation (Equation (1)):

k

i

k

i

k

i

k

j

jiijiiiii xxxxY1 1

1

1 2

2

0

(1)

where Y is the response, Xi, Xj ... Xkj are the factors,

X2i , X

2j , …, X

2k the square effects, XiXj, XiXk and

XjXk the interaction effects, β0 the intercept term, βi

(i=1, 2, ..., k) the linear effect, βii (i=1, 2, ..., k) the

squared effect, βij (i=1, 2, ..., k; j=1, 2, ..., k) the

interaction effect, ε a random error and k the number of

studied factors. The model terms were confirmed or

declined in terms of the probability (P) value with a

95% confidence level. Using the Design Expert

software, the results were completely analyzed via the

analysis of variance (ANOVA). Dimensional plots and

their related contour plots were obtained based on the

effect of the two factors levels. The simultaneous

interaction of the two factors on the responses was

studied in terms of the three dimensional plots [37].

2. 7. Batch Adsorption Experiments The

effect of various parameters (contact time, adsorbent

dosage and temperature) had considered and the

conditions of maximum amount of diazinon removal in

the batch method had determined. In the adsorption tests, 100 mL of the diazinon

solution was prepared from the dilution of 1 g/L stock

solutions. Batch sorption experiments were performed

at a constant temperature of 20 °C on a magnetic mixer

at 400 rpm. At the end of the predetermined time

intervals, the mixture was filtered and the filtrate was

analyzed for any residual diazinon. Each experiment

was performed in duplicate so that we could observe the

reproducibility and the mean value used for each set of

values. The experimental error was below 4%. The

efficiency of diazinon, % removal was calculated as:

(2)

where Ci is the initial concentration (mg/ L) and Cf is

the final concentration (mg/ L).

3. RESULTS AND DISCUSSION 3. 1. Characterization of Sodalite Zeolite The XRD, SEM, Energy dispersive X-ray (EDX) and

FTIR contributed in the synthesized sodalite zeolites

characterization. Figure 2 (a-b) shows the XRD powder

pattern of synthesized sodalite and Cu modified

sodalite. At 2θ values of 14.1°, 20.1°, 22.4°, 24.5°,

27.7°, 31.9°, 34.9°, 37.9°, 43.1°, 45.7° and 48.1°, the

crystallization products matched sodalite characteristic

peaks which were all given by Treacy and Higgins [38],

indicating a successful synthesis of sodalite nanozeolite

with good crystallinity and Cu modified sodalite

exhibits a Cu2O phase which can be assigned at

2θ=38.5o. This result is in agreement with previous

works [39-41]. The SEM image of crystalline phase

yields a useful technique to determine the size and

morphology of the obtained crystals. SEM image of

modified sodalite is shown in Figure 3, suggesting that

formation of the spherical nano sized particle with an

average diameter of 60-80 nm can be observed. Figure 4

presents the elemental analysis of Cu modified analcime

by means of energy dispersive X-ray (EDX). The

presence of Cu peaks in EDX spectrum is explained in

detail and the amounts of elements are given in Table 4.

To obtain the vibrational information on the species in

H. Esfandian et al. / IJE TRANSACTIONS A: Applications Vol. 28, No. 11, (November 2015) 1552-1563 1556

materials, the FT-IR spectroscopy is a very useful

technique. Thus, Figure 5(a-b) depicts the FT-IR

analysis of modified sodalite in the 600–4200 cm-1

range for products in before and after sorption. The

FTIR spectrum of modified zeolite before diazinon

adsorption shows a wide band located at 736.7 cm-1

corresponding to the vibration of Al–O fragment. Also,

the strong broad band at 985.4 cm-1 is connected to the

T–O band (T = silica or aluminum) and its sharpness

suggest fine crystallization of the zeolitic product [42].

The peak at 1658.5 cm–1 is attributed to the free water

bending vibration. The strong broad band at 3400-3700

cm-1 (centered at 3459.7 cm-1) is symbolized by the

stretching of water molecules adsorbed on OH groups

[43-46]. It is observed from the Figure 5 (a-b) that after

adsorbing diazinon in modified zeolite, there are slight

changes in the absorption peak frequencies, suggesting

that a diazinon binding process is taking place on the

surface of the zeolite.

Figure 2. X-ray diffraction of synthesized sodalite zeolite (a)

sodalite (b) Cu modified sodalite.

Figure 3. Scanning electron micrographs of modified sodalite

zeolite.

Figure 4. The EDX spectrum for Copper modified sodalite.

TABLE 4. Amounts of the elements obtained by EDX

analysis.

Element Wt.%

O 49.13

Na 17.07

Al 14.55

`Si 14.75

Cu 4.5

Figure 5. FTIR spectrum of modified sodalite zeolite a- befor

sorption b- after sorption.

3. 2. Statistical Analysis The analysis of

variance ANOVA based on the RSM approach was

performed for the second-order response surface model.

The results related to all responses are presented in

Table 5. Based on Equation (1), the F-values and those

of probability >F show the significance of each

coefficient [47]. The larger the magnitude of the F-

values, the smaller the p-values and the more significant

the corresponding coefficients. The “prob > F” values

were less than 0.05. This also indicates a high

significant regression at 95% confidence level [48]. In

this case, the factors first-order effects, square effects

and interaction effects were specified as significant

model terms. However, the F-value of the model was

selected in the range of 50.82 for diazinon removal

percentage and prob >F (<0.0001) values for all

responses model which suggests that the model terms

are significant. The prob >F value is less than 0.05. This

reveals that the model terms are regarded as statistically

significant. The insignificant value resulting from the

lack of fit (more than 0.05) shows that the quadratic

model is valid for response. The examination of the fit

summary output suggests that the quadratic model is

statistically significant for the response and thus was

used for further analysis [35]. Through the

determination of the coefficient (R2), the model

goodness of fit was checked [37]. Table 6 represents the

empirical relationship between responses (Y) and the

three variables (factors) in coded values obtained by the

application of RSM. The Prob > F values were less than

0.05. This suggests that the model terms, as presented in

this table, are significant. Therefore, in this paper, Y

indicates the responses; A, B and C are the coded values

of the variables, i.e. contact time (min); amount of

1557 H. Esfandian et al./ IJE TRANSACTIONS A: Applications Vol. 28, No. 11, (November2015) 1552-1563

adsorbent (g) and temperature (oC) respectively. For

each response, the square of correlation coefficient was

computed as the R square (R2) that is a measure of the

amount of variation around the mean explained by the

model [33, 37] The value of the coefficient of determination

obtained in this case was 0.9883 for response, indicating

the statistical significance of this regression and only 0-

4% of the total variations are not explained by the

model. The value of the predicted determination of the

coefficient (Pred. R2=0.9252) is in reasonable accord

with the value of adjusted determination of the

coefficient (Adj. R2=0.9777). However, a relatively

lower value of the coefficient of variance (CV=0.95%)

for response suggests a good precision and reliability in

the conducted experiments

TABLE 5. An analysis of variance (ANOVA) for the response surface quadratic model.

Source Sum of Squares Df Mean Square F Value p-value Prob > F

Model 11332.16 9 1259.13 93.77 < 0.0001 Significant

A- contact time 3329.53 1 3329.53 247.95 < 0.0001

B- amount of dosage 1621.04 1 1621.04 120.72 < 0.0001

C-Temp 722.16 1 722.16 53.78 < 0.0001

AB 341.78 1 341.78 25.45 0.0005

AC 81.73 1 81.73 6.09 0.0333

BC 0.21 1 0.21 0.015 0.0034

A2 882.25 1 882.25 65.70 < 0.0001

B2 586.70 1 586.70 43.69 < 0.0001

C2 57.34 1 57.34 4.27 0.0457

Residual 134.28 10 13.43

Lack of Fit 110.29 5 22.06 4.60 0.0598 not significant

Pure Error 23.99 5 4.80

Cor Total 11466.45 19

TABLE 6. ANOVA results for response parameters.

Standard deviation 0.85 R-Squared 0.9883

Mean 67.03 Adj R-Squared 0.9797

C.V.% 0.95 Pred R-Squared 0.9511

PRESS 560.25 Adeq Precision 33.69

3. 3. The Effect of Operation Parameters of the Model on the Removal Efficacy of Diazinon To study the effect of each variable on the response, the

3-D diagrams and counter were used. Moreover, the

interaction diagrams were applied to illustrate the two

parameters interaction effect on each other and on the

removal of diazinon. Figure 6 depicts the overall effect

of parameters. As shown in Figure 6, the effect of

changing contact time on diazinon removal suggests

that the increase in contact time leads to a rapid increase

of yield. Changing the adsorbent material has some

effect on the removal yield of diazinon which is

indicative of the fact that increasing the adsorbent

material leads to an increase in the diazinon removal

yield. The effect of temperature on diazinon removal

yield suggested that increasing the temperature leads to

a linear decrease of the diazinon removal yield in the

model. 3. 4. Effect of Contact Time on the Response The aim of this research is to study the effect of a

variable i.e. contact time on the system response or

absorption and adsorbent processes. In every batch

treatment system, contact time has a meaningful effect

on the final yield of the system and the overall capacity

of the treatment system.

H. Esfandian et al. / IJE TRANSACTIONS A: Applications Vol. 28, No. 11, (November 2015) 1552-1563 1558

Figure 6. The overall diagram of the effect of parameter

deviations on diazinon removal.

Figure 7. Diagram of the contact time effect on the removal

yield of diazinon

Figure 8. Contour and 3-D diagram of the effect of contact

time and adsorbent dosage on diazinon removal efficacy.

In treatment systems related to other processes, the

time or waste time in each section may have effects on

the system overall capacity and efficacy. In fact, one

can mention that the time of every part of the system

and the respective sensitivity to deviation, have a direct

and/or indirect effect on all costs and yields. It should

be noted that the contact time in the absorption process

is considered a parameter of equilibrium. This signifies

that the variable was optimized and the subsequent

increase in the point of equilibrium has no effect on the

absorption process.

Figure 9. Contour and 3-D diagram of effect of Contact Time

and Temperature on diazinon removal efficacy.

Therefore, future studies should be conducted on the

variables that are of high importance and necessity.

Figure 7 illustrates the effect of contact time on the

removal yield of diazinon. As can be seen from the

figure, any increase in contact time leads to an increase

in the diazinon removal efficacy which has a slight

slope. When the equilibrium value is reached, it stays

constant and no increase or decrease occurs. Using 3-D

and contour diagrams of contact time, Figure 8 depicts

the existing effect on contact time and adsorbent mass

in the removal of diazinon from aqueous solution by

modified zeolite adsorbent. It was observed that the

temperature value was constant and equaled 35 oC and

the removal yield was between 39.6497 to 83.7842.

Clearly, the increase in contact time and adsorbent

amount leads to an increase in the removal yield of

diazinon with a 30min contact time and an adsorbent

amount of 0.3. Figure 9, through 3-D diagrams and the

contact time contour effect on the temperature,

illustrates the removal of diazinon indices from aqueous

solution by modified zeolite. It is evident whenever the

amount of adsorbent is constant and equals the mean

value of 0.17 (g) and the removal efficiency is between

48.9461 and 86.53, any increase in contact time leads to

a simultaneous decrease in the temperature, an increase

in the diazinon removal efficacy at contact time=30

min, an temperature of 20 oC and a maximum removal

yield of diazinon of 86.53.

3. 5. The Effect of the Adsorbent Amount of the Model on the Responses The next operational

parameter or system variable which was studied is the

amount of adsorbent in waste samples. The adsorption

process taking place on molecules and adsorbent

surfaces and the availability of absorbable surface have

meaningful effect on removal yield. In addition, this

parameter, like other operational parameters, has both

direct and indirect effect on the processes and costs and

1559 H. Esfandian et al./ IJE TRANSACTIONS A: Applications Vol. 28, No. 11, (November2015) 1552-1563

other related problems of the treatment system. In fact,

the quantity of adsorbent is the main factor in selecting

or not selecting the material in a system based on the

absorption because the overall cost of preparation and

use of the material, and if necessary washing or

excretion, could bring economic benefits and/or be

unaffordable. Consequently, these parameters must be

studied carefully. Figure 10 illustrates the effect of

increase in the quantity of adsorbent on the removal

yield of diazinon. As can be seen, increasing the amount

of absorbent leads to a linear increase in the removal

yield of diazinon in the model. It is readily understood

that the availability of larger surface area and more

adsorption sites increase upon every increase in the

adsorbent dose.

At higher concentrations of sorbate, the equilibrium

uptake did not increase significantly with the increase in

zeolite. Another reason may be the particle interaction

such as aggregation which results from high adsorbent

dose. Such aggregation would lead to a decrease in the

total surface area of the adsorbent and an increase in

diffusional path length [49]. Figure 11 illustrates 3-D

and contour diagrams of the effect of amount of

adsorbent and temperature on removal efficiency of

diazinon which occurs when the contact time reaches 16

min. It can be seen that with the increase in amount of

adsorbent and decrease in temperature, the diazinon

sorption increases and the maximum diazinon removal,

i.e. 84.53 is achieved.

3. 6. Effect of Temperature on the Responses Solution temperature exerts a significant influence on

the rate of adsorption. The temperature has two major

effects on the sorption process. Increasing the

temperature will increase the rate of adsorbate diffusion

across the external boundary layer and in the internal

pores of the adsorbent particles because liquid viscosity

decreases as temperature increases. In addition, temperature affects the equilibrium

capacity of the adsorbent depending on weather the

interaction between the adsorbent and the adsorbate is

exothermic or endothermic. The results indicate that the

adsorption capacity of modified zeolite with respect to

diazinon decreases with increase in temperature,

indicating that the adsorption process is exothermic in

nature. Figure 12 shows the changing parameter of

temperature merely on diazinon removal yield. It is

clear that the increase in temperature leads to a decrease

in sorption.

3. 7. Process Optimization The last target of

the response surface methodology is to find the

optimum operating condition for the process. The

purpose of this optimization is to achieve maximum

diazinon removal efficiency alongside the best

conditions of variables.

Figure 10. Diagram of effect of absorbent amount on diazinon

removal yield.

Figure 11. Contour and 3-D diagrams of effect of absorbent

amount and temperature on diazinon sorption.

The optimized condition preset at high and low level

or in the ranges of the process variables. Table 7 depicts

the optimization criteria used to obtain the optimal

region for the response. To carry out the optimization

phase, the desirability function procedure was used. The

desirability function procedure is one of the widely used

multi-response optimization methods in practice that

locates one or more points that maximize this function.

The desirability lies between 0 and 1 and it is indicative

of the proximity of a response to its ideal value [50, 51].

Table 8 shows the results of the optimum conditions.

In Table 8, the best optimum condition of process

variables is number 1 which has the highest value of

desirability. Figure 13 shows the desirability for each

parameter and response solely for the best optimal

conditions. As discussed earlier, for optimizing

response, simultaneous objective function is a geometric

mean of all transformed responses.

The highest desirability was observed for efficiency.

That means that efficiency is closer to its target value.

Additional experiments were performed at 5 optimum

conditions to verify the accuracy optimal conditions

produced by the design expert software. The

H. Esfandian et al. / IJE TRANSACTIONS A: Applications Vol. 28, No. 11, (November 2015) 1552-1563 1560

comparison of the experimental values and the

software-predicted ones is presented in Table 9. Results

suggest there is a mean error of 1.246%.

TABLE 7. Parameters of the Response Optimization

Criteria Target Lower limit Upper limit

Contact time(min) Is in range 16 30

Amount of adsorbent(g) Is in range 017 0.3

Temperature(oC) Is in range 20 35

Diazinon removal, % Maximize 19.54 96.71

Figure 12. Diagram of the effect of the temperature on the

removal yield of diazinon.

TABLE 8. Optimum diazinon removal condition

No. Contact time (min) Amount of adsorbent (g) Temperature (oC) Diazinon removal Desirability

1 23.62 0.22 29.28 97.24 1

2 22.89 0.23 21.68 99.74 1

3 25.01 0.19 21.15 97.54 1

4 29.35 0.24 27.72 97.17 1

5 21.84 0.25 22.98 99.06 1

6 25.54 0.27 29.19 97.56 1

7 24.98 0.27 30.19 97.33 1

8 24.36 0.23 30.56 97.18 1

9 26.42 0.26 31.43 96.74 1

10 21.19 0.27 26.86 96.73 1

TABLE 9. Verification Experiments at Optimum Condition

Diazinon removal (%) Run

Error% Predicted (DoE) Experimental

0.93 97.24 96.34 1

2.3 99.74 97.45 2

1.46 97.54 96.14 3

0.37 97.17 97.53 4

1.17 99.06 97.91 5

1.246 Mean error%

Figure 13. Desirability of the optimum condition for the best

optimal conditions.

4. CONCLUSION

Sodalite zeolite crystals can be successfully synthesized

from perlite in an alkaline solution. Results suggested

that the modified zeolite have considerable potential for

the removal of diazinon from aqueous solutions. The

studies carried out on the adsorption onto modified

zeolite with Cu2O nanoparticles proved to be highly

effective in the sorption of diazinon. To obtain the

operational conditions for the maximum removal rates

of this substance, the RSM experimental design was

used. The employed variables for wastewater treatment

process were contact time (min), amount of adsorbent

(g) and temperature (oC). These variables were studied

in laboratory and the optimum value of each variable

was determined to reach the maximum value of

responses. Results suggested that the optimum

conditions were 97.86 % diazinon removal in batch

runs. The results of the analysis using the Design Expert

showed that the obtained approximation models for

diazinon were satisfactorily fitted with R2 of 0.9883.

1561 H. Esfandian et al./ IJE TRANSACTIONS A: Applications Vol. 28, No. 11, (November2015) 1552-1563

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1563 H. Esfandian et al./ IJE TRANSACTIONS A: Applications Vol. 28, No. 11, (November2015) 1552-1563

Removal of Diazinon from Aqueous Solutions in Batch Systems Using Cu-modified

Sodalite zeolite: an Application of Response Surface Methodology H. Esfandiana, M. Parvinia, B. Khoshandama, A. Samadi-Maybodib a Faculty of Chemical, Oil and Gas Engineering, Semnan University, Semnan, Iran.

b Analytical Division, Faculty of Chemistry, University of Mazandaran. Babolsar, Iran

P A P E R I N F O

Paper history: Received 03 October 2015 Received in revised form 05 November 2015 Accepted 19 November 2015

Keywords: Diazinon Adsorption Sodalite Zeolite Batch Systems RSM

هچكيد

در این پژوهش از پرلیت به عنوان منبع کم ارزش سیلیس و آلومینیوم در سنتز زئولیت سودالیت با روش هیدروترمال استفاده

زئولیت اصالح شده به عنوان جاذب از ( بر روی سطح زئولیت قرار گرفته و سپسCu2Oشده است. نانو ذرات اکسید مس )

. به منظور بررسی فرآیند تصفیه سه فاکتور مهم زمان تماس، جرم جاذب و دما شد ادهدر حذف دیازینون از محلول آبی استف

( طراحی و در CCD( با استفاده از نقاط مرکزی )RSMموردنظر بودند. تاثیر فاکتورهای ذکر شده با روش جذب سطحی )

گراد درجه سانتی 62/62گرم و دما 66/0دقیقه، جرم جاذب 26/62آزمایشات استفاده شدند. شرایط بهینه شامل زمان تماس

به دست آمده است. %62/29راندمان حذف دیازینون در سیستم ناپیوسته صد دربوده است و

doi:10.5829/idosi.ije.2015.28.11b.02